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In recent years, influencer marketing has taken over traditional brand advertisements on social media platforms, combining word-of-mouth marketing with celebrity endorsements. However, there has been limited academic research on the key success factors in influencer marketing. This paper used a hybrid MCDM model that integrates the Delphi method and the decision-making trial and evaluation laboratory (DEMATEL) approach. Through a two-stage empirical study, the research aims to explore the crucial success factors in influencer marketing. This study focuses on the Taiwanese market. The target respondents for the survey are consumers in Taiwan who have purchased products or services based on recommendations from key opinion leaders (KOLs) or internet celebrities. This systematic research framework not only pinpoints key factors that capture consumer attention towards influencers, but it also illustrates the inter-relationship of structure and improvement directions among these factors. According to results, the key factors include influencer reputation, credibility, degree of key opinion influence, attractiveness, popularity, consistency between influencers and brands, fan engagement level, and informativeness. Finally, businesses can consider five aspects to be the purpose of collaboration, product type, target audience for the product, characteristics of the influencer's creative content, and media attributes. By comprehensively considering these aspects, businesses can determine the appropriate format for collaborative content. This decision can then guide how influencers communicate with consumers, effectively conveying brand information to the relevant target audience. The research findings provide fresh and significant insights in the field of influencer marketing studies. [ABSTRACT FROM AUTHOR]

We investigate the association of a sensitive characteristic or latent variable with observed binary random variables by the randomized response (RR) technique of Warner in his publication (Warner, S.L. J. Am. Stat. Assoc.1965, 60, 63–69) and a latent class model. First, an expectation-maximization (EM) algorithm is provided to easily estimate the parameters of the null and alternative/full models for the association between a sensitive characteristic and an observed categorical random variable under the RR design of Warner's paper above. The likelihood ratio test (LRT) is utilized to identify observed categorical random variables that are significantly related to the sensitive trait. Another EM algorithm is then presented to estimate the parameters of a latent class model constructed through the sensitive attribute and the observed binary random variables that are obtained from dichotomizing observed categorical random variables selected from the above LRT. Finally, two classification criteria are conducted to predict an individual in the sensitive or non-sensitive group. The practicality of the proposed methodology is illustrated with an actual data set from a survey study of the sexuality of first-year students, except international students, at Feng Chia University in Taiwan in 2016. [ABSTRACT FROM AUTHOR]

According to the World Energy Investment 2018 report, the global annual investment in renewable energy exceeded USD 200 billion for eight consecutive years until 2017. In this paper, a deep-learning-based time-series prediction method, namely a gated recurrent unit (GRU)-based prediction method, is proposed to predict energy generation in Taiwan. Data on thermal power (coal, oil, and gas power), renewable energy (conventional hydropower, solar power, and wind power), pumped hydropower, and nuclear power generation for 1991 to 2020 were obtained from the Bureau of Energy, Ministry of Economic Affairs, Taiwan, and the Taiwan Power Company. The proposed GRU-based method was compared with six common forecasting methods: autoregressive integrated moving average, exponential smoothing (ETS), Holt–Winters ETS, support vector regression (SVR), whale-optimization-algorithm-based SVR, and long short-term memory. Among the methods compared, the proposed method had the lowest mean absolute percentage error and root mean square error and thus the highest accuracy. Government agencies and power companies in Taiwan can use the predictions of accurate energy forecasting models as references to formulate energy policies and design plans for the development of alternative energy sources. [ABSTRACT FROM AUTHOR]

An unrelated parallel machine scheduling problem motivated by the scheduling of a printed circuit board assembly (PCBA) under surface mount technology (SMT) is discussed in this paper. This problem involved machine eligibility restrictions, sequence-dependent setup times, precedence constraints, unequal job release times, and constraints of shared resources with the objectives of minimizing the makespan and the total job tardiness. Since this scheduling problem is NP-hard, a mathematical model was first built to describe the problem, and a heuristic approach using a non-dominated sorting genetic algorithm (NSGA-II) was then designed to solve this bi-objective problem. Multiple near-optimal solutions were provided using the Pareto front solution and crowding distance concepts. To demonstrate the efficiency and effectiveness of the proposed approach, this study first tested the proposed approach by solving test problems on a smaller scale. It was found that the proposed approach could obtain optimal solutions for small test problems. A real set of work orders and production data was provided by a famous hardware manufacturer in Taiwan. The solutions suggested by the proposed approach were provided using Gantt charts to visually assist production planners to make decisions. It was found that the proposed approach could not only successfully improve the planning time but also provide several feasible schedules with equivalent performance for production planners to choose from. [ABSTRACT FROM AUTHOR]

Amid rising global demand for renewable energy, geothermal power emerges as a vital, low-carbon solution to enhance energy security and sustainability. Taiwan, strategically located on the seismically active Pacific Ring of Fire, possesses an untapped geothermal potential that is underutilized due to complex site selection challenges. This study specifically addresses the need for a more precise and psychologically attuned site selection process, aiming to optimize the development of geothermal resources in regions with complex geological settings. Utilizing the Modified Bounded Rationality Data Envelopment Analysis (MB-DEA) model, this research integrates traditional DEA with bounded rationality to factor in the risk preferences of decision-makers, offering a novel approach that enhances accuracy in evaluating geothermal sites. This study addresses the critical challenge of accurately selecting geothermal energy sites in geologically complex regions like Taiwan, where traditional methods fall short, aiming to significantly boost the efficiency and effectiveness of geothermal energy exploitation as part of Taiwan's transition to renewable energy sources. Applied to 30 potential sites across Taiwan, our model provides a detailed assessment based on technical, economic, and psychological criteria, revealing variations in site suitability influenced by stakeholder risk attitudes. Key locations such as Datun Mountain, Maoxing, and Taolin consistently rank highly, confirming their robust potential irrespective of risk preferences. At the same time, other sites show marked sensitivity to shifts in decision-making attitudes. This work significantly advances the methodology of renewable energy site selection by demonstrating the utility of incorporating psychological factors into analytical models, which not only refines decision-making processes but also aligns with Taiwan's strategic energy planning goals. This study also underscores the importance of accurate geographical data in complex terrains, suggesting further refinement and dynamic integration of bounded rationality for future research. [ABSTRACT FROM AUTHOR]

A well-established financial trading system should well perform in resource allocation, risk management, and sustainability. In this paper, we propose a self-management portfolio system with adaptive association mining for practical applications. The system allocates funds into independent units for risk management, and utilizes association mining and adaptive closing mechanism for resource allocation and sustainability, and adopts a self-management module for monitoring positions. The proposed system boosts the annual return and Sharpe ratio to 9.1% and 0.578 (increased to 2.28 and 2.48 times), and reduces the drawdown risk to 34.6% (decreased to almost half). Furthermore, the system rapidly closes the stock positions to avoid drawdown risk in the bear markets, and gradually increases the stock positions when the market turns into bull. Compared with benchmarks, proposed system outperforms all benchmarks in all measurements and on randomly sampled dataset. [ABSTRACT FROM AUTHOR]

This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed. [ABSTRACT FROM AUTHOR]

Stock prediction has garnered considerable attention among investors, with a recent focus on the application of machine learning techniques to enhance predictive accuracy. Prior research has established the effectiveness of machine learning in forecasting stock market trends, irrespective of the analytical approach employed, be it technical, fundamental, or sentiment analysis. In the context of fiscal year-end selection, the decision may initially seem straightforward, with December 31 being the apparent choice, as discussed by B. Kamp in 2002. The primary argument for a uniform fiscal year-end centers around comparability. When assessing the financial performance of two firms with differing fiscal year-ends, substantial shifts in the business environment during non-overlapping periods can impede meaningful comparisons. Moreover, when two firms merge, the need to synchronize their annual reporting often results in shorter or longer fiscal years, complicating time series analysis. In the US S&P stock market, misaligned fiscal years lead to variations in report publication dates across different industries and market segments. Since the financial reporting dates of US S&P companies are determined independently by each listed entity, relying solely on these dates for investment decisions may prove less than entirely reliable and impact the accuracy of return prediction models. Hence, our interest lies in the synchronized fiscal year of the TW stock market, leveraging machine learning models for fundamental analysis to forecast returns. We employed four machine learning models: Random Forest (RF), Feedforward Neural Network (FNN), Gated Recurrent Unit (GRU), and Financial Graph Attention Network (FinGAT). We crafted portfolios by selecting stocks with higher predicted returns using these machine learning models. These portfolios outperformed the TW50 index benchmarks in the Taiwan stock market, demonstrating superior returns and portfolio scores. Our study's findings underscore the advantages of using aligned financial ratios for predicting the top 20 high-return stocks in a mid-to-long-term investment context, delivering over 50% excess returns across the four models while maintaining lower risk profiles. Using the top 10 high-return stocks produced over 100% relative returns with an acceptable level of risk, highlighting the effectiveness of employing machine learning techniques based on financial ratios for stock prediction. [ABSTRACT FROM AUTHOR]

Fatigue is a prevalent subjective sensation, affecting both office workers and a significant global population. In Taiwan alone, over 2.6 million individuals—around 30% of office workers—experience chronic fatigue. However, fatigue transcends workplaces, impacting people worldwide and potentially leading to health issues and accidents. Gaining insight into one's fatigue status over time empowers effective management and risk reduction associated with other ailments. Utilizing photoplethysmography (PPG) signals brings advantages due to their easy acquisition and physiological insights. This study crafts a specialized preprocessing and peak detection methodology for PPG signals. A novel fatigue index stems from PPG signals, focusing on the dicrotic peak's position. This index replaces subjective data from the brief fatigue index (BFI)-Taiwan questionnaire and heart rate variability (HRV) indices derived from PPG signals for assessing fatigue levels. Correlation analysis, involving sixteen healthy adults, highlights a robust correlation (R > 0.53) between the new fatigue index and specific BFI questions, gauging subjective fatigue over the last 24 h. Drawing from these insights, the study computes an average of the identified questions to formulate the evaluated fatigue score, utilizing the newfound fatigue index. The implementation of linear regression establishes a robust fatigue assessment system. The results reveal an impressive 91% correlation coefficient between projected fatigue levels and subjective fatigue experiences. This underscores the remarkable accuracy of the proposed fatigue prediction in evaluating subjective fatigue. This study further operationalized the proposed PPG processing, peak detection method, and fatigue index using C# in a computer environment alongside a PPG device, thereby offering real-time fatigue indices to users. Timely reminders are employed to prompt users to take notice when their index exceeds a predefined threshold, fostering greater attention to their physical well-being. [ABSTRACT FROM AUTHOR]

We present a nonautonomous compartmental model that incorporates vaccination and accounts for the seasonal transmission of typhoid fever. The dynamics of the system are governed by the basic reproductive number R 0 . This demonstrates the global stability of the disease-free solution if R 0 < 1 . On the contrary, if R 0 > 1 , the disease persists and positive periodic solutions exist. Numerical simulations validate our theoretical findings. To accurately fit typhoid fever data in Taiwan from 2008 to 2023, we use the model and estimate its parameters using Latin hypercube sampling and least squares techniques. A sensitivity analysis reveals the significant influence of parameters such as infection rates on the reproduction number. Increasing vaccination coverage, despite challenges in developing countries, reduces typhoid cases. Accessible and highly effective vaccines play a critical role in suppressing the epidemic, outweighing concerns about the efficacy of the vaccine. Investigating possible parameter changes in Taiwan highlights the importance of monitoring and managing transmission rates to prevent recurring annual epidemics. [ABSTRACT FROM AUTHOR]

Renewable energy integration has been recently promoted by many countries as a cleaner alternative to fossil fuels. In many research works, the optimal allocation of distributed generations (DGs) has been modeled mathematically as a DG injecting power without considering its intermittent nature. In this work, a novel probabilistic bilevel multi-objective nonlinear programming optimization problem is formulated to maximize the penetration of renewable distributed generations via distribution network reconfiguration while ensuring the thermal line and voltage limits. Moreover, solar, wind, and load uncertainties are considered in this paper to provide a more realistic mathematical programming model for the optimization problem under study. Case studies are conducted on the 16-, 59-, 69-, 83-, 415-, and 880-node distribution networks, where the 59- and 83-node distribution networks are real distribution networks in Cairo and Taiwan, respectively. The obtained results validate the effectiveness of the proposed optimization approach in maximizing the hosting capacity of DGs and power loss reduction by greater than 17% and 74%, respectively, for the studied distribution networks. [ABSTRACT FROM AUTHOR]

Airborne particulate matter 2.5 (PM2.5) can have a profound effect on the health of the population. Many researchers have been reporting highly accurate numerical predictions based on raw PM2.5 data imported directly into deep learning models; however, there is still considerable room for improvement in terms of implementation costs due to heavy computational overhead. From the perspective of environmental science, PM2.5 values in a given location can be attributed to local sources as well as external sources. Local sources tend to have a dramatic short-term impact on PM2.5 values, whereas external sources tend to have more subtle but longer-lasting effects. In the presence of PM2.5 from both sources at the same time, this combination of effects can undermine the predictive accuracy of the model. This paper presents a novel combinational Hammerstein recurrent neural network (CHRNN) to enhance predictive accuracy and overcome the heavy computational and monetary burden imposed by deep learning models. The CHRNN comprises a based-neural network tasked with learning gradual (long-term) fluctuations in conjunction with add-on neural networks to deal with dramatic (short-term) fluctuations. The CHRNN can be coupled with a random forest model to determine the degree to which short-term effects influence long-term outcomes. We also developed novel feature selection and normalization methods to enhance prediction accuracy. Using real-world measurement data of air quality and PM2.5 datasets from Taiwan, the precision of the proposed system in the numerical prediction of PM2.5 levels was comparable to that of state-of-the-art deep learning models, such as deep recurrent neural networks and long short-term memory, despite far lower implementation costs and computational overhead. [ABSTRACT FROM AUTHOR]

Reducing the potential risks in the manufacturing process to improve the reliability of the switched-mode power supply (SMPS) is a critical issue for the users' safety. This paper proposes a novel failure mode and effects analysis (FMEA) model based on hybrid multiple criteria decision-making (MCDM), which adopts neutrosophic set theory into the proposed model. A developed neutrosophic Best Worst method (NBWM) is used to evaluate the weights of risk factors and determine their importance. Secondly, the neutrosophic Weight Aggregated Sum Product Assessments (NWASPAS) method is utilized to calculate the Risk Priority Number (RPN) of the failure modes. The proposed model improves the shortcomings of traditional FMEA and improves the practical applicability and effectiveness of the Best Worst method (BWM) and Weight Aggregated Sum Product Assessments (WASPAS) methods. In addition, this study uses neutrosophic logic to reflect the true judgments of experts in the assessment, which considers authenticity, deviation, and uncertainty to obtain more reliable information. Finally, an empirical case study from an SMPS company headquartered in Taiwan demonstrates the effectiveness and robustness of the proposed model. In addition, by comparing with two other FMEA models, the results show that the proposed model can more clearly reflect the true and effective risks in the assessment. The results can effectively help power supply manufacturers to assess risk factors and determine key failure modes. [ABSTRACT FROM AUTHOR]

The newly added requirement in Taiwan's Money Laundering Control Act to fully authenticate customers' identity by regulated entities, particularly to reveal substantial beneficial owners, entails greater disclosure of customers' private information. How to strike the balance between such disclosure and information privacy protection has not been mentioned yet in Taiwan. The goal of this research paper is to identify support measures, consisting of criteria, sub-criteria, and alternatives, for information privacy protection for future change. A questionnaire was developed through applying the modified Delphi method. This study also applied the analytic hierarchy process to the questionnaire to prioritize the importance of different criteria and sub-criteria and find the optimal alternative. The search results indicated that the criteria were (in order of importance) internal control in financial institutions, prior surveillance mechanisms, informed consent, and safe-harbor immunity. Further examination of the details for the sub-criteria indicated that the establishment of an external independent supervising mechanism and the adoption of a personal information impact assessment are increasingly prioritized for implementation. Moreover, Alternative 2 ("Public and private entities should implement criteria and sub-criteria simultaneously") weighed 0.62 more in terms of importance compared with Alternative 1 ("Public entities should implement criteria and sub-criteria first"), which weighed 0.38. [ABSTRACT FROM AUTHOR]

Even sustainable organizations have received overwhelming attention, but there is a lack of studies to explore the key success factors for sustainable traditional manufacturing based on expert opinions. The purpose of this study was to explore the key success factors for sustainable development in traditional industries through expert knowledge. In this study, the Delphi method was applied to construct the research framework with the most appropriate criteria. Moreover, we proposed an effective solution based on the Decision-Making Trial and Evaluation Laboratory (DEMATEL)-based Analytic Network Process (ANP) to determine the correlation and causality of these factors based on the decision laboratory method for multi-criteria decision-making. We also integrated the importance–performance analysis to illustrate the attributes improvement priorities. Our results show that managers and policy-makers should concentrate more on knowledge management to enhance the sustainability of organizations. Moreover, managers should keep teamwork and employee engagement at a high level to achieve the goal of organizations. Additionally, the theoretical and practical implications provide five priority indicators for the success of a sustainable organization. [ABSTRACT FROM AUTHOR]

Real estate is a complex and unpredictable industry because of the many factors that influence it, and conducting a thorough analysis of these factors is challenging. This study explores why house prices have continued to increase over the last 10 years in Taiwan. A clustering analysis based on a double-bottom map particle swarm optimization algorithm was applied to cluster real estate–related data collected from public websites. We report key findings from the clustering results and identify three essential variables that could affect trends in real estate prices: money supply, population, and rent. Mortgages are issued more frequently as additional real estate is created, increasing the money supply. The relationship between real estate and money supply can provide the government with baseline data for managing the real estate market and avoiding unlimited growth. The government can use sociodemographic data to predict population trends to in turn prevent real estate bubbles and maintain a steady economic growth. Renting and using social housing is common among the younger generation in Taiwan. The results of this study could, therefore, assist the government in managing the relationship between the rental and real estate markets. [ABSTRACT FROM AUTHOR]

In this study, a mobile learning system (MLS) was developed and adopted to facilitate elementary second-grade students to learn mathematics. A quasi-experimental design was adopted. There were two learning models, including the typical instruction group (TI group) and MLS group. The learning content is the topic of multiplication. A total of 93 s-grade students from four classes in a public elementary school in Northern Taiwan participated in this research. Participants were randomly divided into the MLS group (47 participants: 22 boys and 25 girls) and the TI group (46 participants: 26 boys and 20 girls). Participants in the MLS group received mathematics instruction in the MLS, whereas those in the TI group received direct instruction in typical classrooms. All students took the pretest and posttest of mathematics learning achievement test and mathematics learning interest scale assess their improvement of learning achievement and learning interest after the learning activities. The findings revealed that students in the MLS group had significantly better improvement in their mathematics learning interest and mathematics learning achievement than those in the TI group. In addition, students in the MLS group had significantly better performance in answering items of comprehension and application levels in the mathematics learning achievement test. [ABSTRACT FROM AUTHOR]

In this study, the non-dominated sorting genetic algorithm II (NSGA-II) is used to optimize the annual phase arrangement of distribution transformers connected to primary feeders to improve three-phase imbalance and reduce power loss. Based on the data of advanced metering infrastructure (AMI), a quasi-real-time ZIP load model and typical sample distribution systems in Taiwan are constructed. The equivalent circuit models and solution algorithms for typical distribution systems in Taiwan are built using the commercial software package MATLAB. A series of simulations, analyses, comparisons, and explorations is executed. Finally, the quantitative evaluation results for improving the voltage imbalance and reducing the power loss are summarized. For the series of studies, the percentage reductions in (1) total power imbalance T S I , (2) total line loss T L L , (3) average voltage drop A V D , (4) total voltage imbalance factors for zero/negative sequences T d 0 / T d 2 , and (5) neutral current of the main transformer I L C O are up to 45.48%, 4.06%, 16.61%, 63.99%, 21.33%, and 88.01%, respectively. The results obtained in this study can be applied for energy saving and can aid the authorities to implement sustainable development policies in Taiwan. [ABSTRACT FROM AUTHOR]

The prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Investors put their money into the financial market, hoping to maximize profits by understanding market trends and designing trading strategies at the entry and exit points. Most studies propose machine learning models to predict stock prices. However, constructing trading strategies is helpful for traders to avoid making mistakes and losing money. We propose an automatic trading framework using LSTM combined with deep Q-learning to determine the trading signal and the size of the trading position. This is more sophisticated than traditional price prediction models. This study used price data from the Taiwan stock market, including daily opening price, closing price, highest price, lowest price, and trading volume. The profitability of the system was evaluated using a combination of different states of different stocks. The profitability of the proposed system was positive after a long period of testing, which means that the system performed well in predicting the rise and fall of stocks. [ABSTRACT FROM AUTHOR]

During enterprise foundation and development, internal finance and debt finance are of vital importance to start-up entrepreneurs. Therefore, the purpose of this study is mainly to focus on how start-ups can make the optimal evaluation among different external equity crowdfunding solutions and to establish a network decision support model that evaluates the optimal financing solution of start-ups for external equity crowdfunding based on decision science and network architecture. The Lending Company in Financial Technology Industry (LCFTI) was taken as an example. The results indicate that equity crowdfunding is the optimal financing plan in LCFTI. Academically, the results of this study not only help propose a network decision support model using decision science methods and implementing the network analysis to establish an architecture to evaluate the optimal financing plans of start-ups for external equity crowdfunding, they also makes up for the gap in the optimal financing plans of entrepreneurs or start-ups for external equity financing, which has not been specified in the POT theory in the past. Practically, this study provides a useful tool for the entrepreneur of LCFTI to understand the key factors affecting the optimal financing plans for external equity financing and enables LCFTI to measure the optimal financing plans for external equity financing to improve the success rate of finance. [ABSTRACT FROM AUTHOR]

Globally, in the innovative and distributional circular textiles, the textile sector of Taiwan has a prominent place. Within the textile industry, the circular economy (CE) obstacles adopted have been studied by several scholars. However, the interrelationships among these obstacles are easily ignored. The present study aimed to identify CE adoption obstacles from the supply chain (SC) perspective in Taiwan's textile sector by analyzing the interrelationships among the CE adoption obstacles and establishing a hierarchical network and the causal inter relationships of the identified obstacles. Furthermore, the CE adoption obstacles and interrelationships were analyzed using interpretative structural modeling and the decision-making trial and evaluation laboratory (ISM-DEMATEL). The common results of the two methods demonstrated that two obstacles, consumers not having sufficient knowledge and awareness of reuse/recycling (B1) and a lack of successful business models and frameworks for CE implementation (B3), were the significant obstacles influencing adopting CE in the textile supply chain, while the obstacle making the most efficient way (B12) of the right decision to implement CE was minor. Thus, the government should formulate friendly laws and regulations that encourage CE adoption, while textile firms should monitor and control recycling and efficiency approaches handling the CE adoption problems. Our results could offer first-hand knowledge to textile firms or managers to effetely achieve CE implementation objectives. [ABSTRACT FROM AUTHOR]

To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan's wind power output datasets. [ABSTRACT FROM AUTHOR]

With the advent of science and technology, smart devices have become ubiquitous; since the display unit is a vital component in many smart devices, the Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD) industry has been one of the most rapidly growing industries. Taiwanese manufacturers play a critical role in this industry. This study investigates key factors for supplier selection in Taiwan's TFT-LCD industry. TFT-LCD is a technology-intensive industry. However, few studies in the past considered the technological abilities dimension in supplier selection. Therefore, this study discusses the factors related to the technological abilities dimension in supplier selection. Most research considered supplier selection based on the traditional criteria such as cost and quality. This study discusses the importance of the resilience criteria such as agility and flexibility. A method combining DEMATEL (Decision Making Trial and Evaluation Laboratory) and ANP (Analytic Network Process) is applied to analyze key factors for supplier selection in Taiwan's TFT-LCD industry. The analytical results indicate that the technological abilities dimension and resilience criteria are at the forefront of the ranking in prominence. The influential weights of criteria and the causal diagram among all criteria derived from this study can offer guidance for suppliers on improving various factors to become desirable partners in the TFT-LCD industry supply chain. [ABSTRACT FROM AUTHOR]

Determining how to evaluate the art licensing model is a complex multi-criteria decision making (MCDM) problem. This study explores the critical factors influencing art licensing in the art and creative industry because this industry has potential contributions to a high value-added cultural economy in Taiwan. Due to the high cultural value of the National Palace Museum and the uniqueness of its cultural relics, this study takes it as an empirical case. The decision hierarchy is constructed based on a literature review and expert interviews. Furthermore, we used the fuzzy integral to calculate the weights and overall performance of the three licensing models. The empirical results illustrate that both brand licensing and image licensing groups emphasized the aspect of market environment as the primary consideration for licensing model selection. In particular, respondents considered that the adoption of a brand licensing model could facilitate the achievement of better overall performance value. The reason is that brand licensing made the company's brand visible by imprinting the brand names of both the National Palace Museum and the company on products. This study can be used as reference model for the evaluation of art licensing by enterprises in the future. [ABSTRACT FROM AUTHOR]

The National Health Insurance Administration of Taiwan has implemented global budget payments, the Diagnosis-Related Group (DRG) inpatient diagnosis-related group payment system, and the same-disease payment system, in order to decrease the financial burden of medical expenditure. However, the benefit system reduces the income of doctors and hospitals. This study proposed an early warning payment algorithm that applies data analytics technology to diabetes hospitalization- and treatment-related fees. A model was constructed based on the characteristics of the Exponentially Weighted Moving Average (EWMA) algorithm to develop control charts, which were first employed using the 2001–2017 health insurance statistical database released by the Department of Health Insurance (DHI). This model was used to simulate data from inpatients with diabetes, to create an early warning algorithm for diagnosis-related groups' (DRGs') medical payments as well as to measure its accuracy. This study will provide a reference for the formulation of payment policies by the DHI. [ABSTRACT FROM AUTHOR]

The convenience feature of mobile payment has replaced interactions with physical money and reduced transaction time, which better meets the demand of modern people for convenience in life. As mobile payments play an important role in mobile business, understanding the factors attracting consumers to mobile payment will bring mobile businesses more opportunities for development, and further significantly improve the output value of mobile businesses. This study discusses how to further influence consumer behavioral intention in Taiwan, as based on the main theoretical framework of the Extend Unified Theory of Acceptance and Use of Technology (UTAUT2) and Diffusion of Innovation (DOI). In this study, data analysis is implemented by Partial Least Squares (PLS) for the purpose of verifying the research model and hypotheses. The research results show that social influence, facilitating conditions, hedonic motivation, compatibility, innovation, relative advantage, and observability have positive influence on consumer intention to use mobile payment. [ABSTRACT FROM AUTHOR]

Forecasting the demand for container throughput is a critical indicator to measure the development level of a port in global business management and industrial development. Time-series analysis approaches are crucial techniques for forecasting the demand for container throughput. However, accurate demand forecasting for container throughput remains a challenge in time-series analysis approaches. In this study, we proposed a mixed-precision neural architecture to forecasting the demand for container throughput. This study is the first work to use a mixed-precision neural network to forecast the container throughput—the mixed-precision architecture used the convolutional neural network for learning the strength of the features and used long short-term memory to identify the crucial internal representation of time series depending on the strength of the features. The experiments on the demand for container throughput of the five ports in Taiwan were conducted to compare our deep learning architecture with other forecasting approaches. The results indicated that our mixed-precision neural architecture exhibited higher forecasting performance than classic machine learning approaches, including adaptive boosting, random forest regression, and support vector regression. The proposed architecture can effectively predict the demand for port container throughput and effectively reduce the costs of planning and development of ports in the future. [ABSTRACT FROM AUTHOR]

The socioeconomic costs of occupational accidents represent an important factor in the safety and healthy development of a country's economy. Insurance payments (which cover workplace incidents, such as wounds or illness, disability and fatality) can be considered a proxy for the socioeconomic cost of occupational accidents. Occupational accidents in different industries cause important variations in these three socioeconomic costs—for example, in their frequency and severity. One of the most commonly used mathematical programming approaches that analyze the performance of inputs, economic outputs and occupational accidents is data envelopment analysis (DEA), which has also been used in recent years to estimate the relative performance related to occupational injuries. This study measures the safety and healthy economic performance of Taiwan's 17 industrial sectors by incorporating the varying importance of the three socioeconomic costs of occupational accidents into a weighted DEA Model. The empirical results demonstrate that integrating the varying importance of the three socioeconomic costs of occupational accidents in the evaluation of safety and healthy economic performance is very important. To improve the occupational safety and healthy economic performance of Taiwan's main industrial sectors, efforts should focus on reducing fatalities, which are very costly. These findings could help Taiwan's policy makers effectively improve their safety and healthy economic performance, based on the specific context of each industry, especially the mining and quarrying industry. [ABSTRACT FROM AUTHOR]

Continuous improvement and innovation are solid foundations for the company to maintain excellent performance and competitive advantage. As the limited resources possessed by companies generally result in the incapability of implementing several improving plans simultaneously, researchers advocate that companies should evaluate the influential relationships among key success factors (KSFs) to explore the more dominant determinants for designing improving actions. This study focused on the auto lighting aftermarket (AM) industry in which the KSFs have not yet been adequately performed to explore the decisive criteria of an improvement strategy. After a literature review and a survey of experts, a preliminary list of suitable evaluation criteria was derived. Consequently, the fuzzy and decision-making trial and evaluation laboratory (DEMATEL) method were employed to analyze and establish the causal relationship among criteria. This study contributes to the auto lighting AM industry by using a novel approach for identifying and prioritizing the KSFs. The result indicates that product integrity was the "cause" construct on the constructs of operating cost, quality and brand, technology development, and customer satisfaction. These findings contribute to help practitioners better design effective improvement strategies. [ABSTRACT FROM AUTHOR]

Core competency is the basis of promoting a competitive advantage for any organization; this study aims to establish an objective and systematic assessment model for companies to identify their capabilities. This model will not only assist companies in understanding their own capabilities but will also allow them to compare their performance with that of main competitors to strengthen their competitiveness. The analysis process involved a combination of the fuzzy analytic hierarchy process, fuzzy linguistic value, and the ideal and anti-ideal concept. An objective and systematic model was first developed, which underwent empirical analysis using data from the top three home delivery companies in Taiwan. It was found that "Basic organizational competencies" and "Special competitive competencies" were the two key categories for the home delivery industry. Moreover, "Service price", "On-time delivery", and "Secure delivery" were found to be the three most important capabilities needed for home delivery companies. The results showed that the model is able to effectively assist organizations in constructing or assessing their organizational capabilities; in addition, organizations can adjust their service profiles to adapt to today's turbulent business environment and rapidly changing consumer demands. [ABSTRACT FROM AUTHOR]

The requirements and demand for personal security and public order have increased under great pressure from economic growth and society. This research applied the undesirable output model, which is a mathematical model, to measure the efficiencies of the security department in Taiwan. Further analysis has considered the efficient frontier to classify the efficiency of all 22 counties/cities in Taiwan in 2016, towards a sustainable security environment. The result of this research shown some cities have performed excellent efficiency in the security problem. According to analysis, the efficiency can be improved by decreasing excesses in inputs and bad outputs. This research has evaluated the police departments in Taiwan comprehensively and differentiated the efficiency in safety management of all police departments in Taiwan. [ABSTRACT FROM AUTHOR]